%0 Journal Article
%T 基于欧拉反褶积的基底断裂研究
Research on Basement Fault Using Euler Deconvolution
%A 高子龙
%A 龚峰
%A 梁田
%A 许少帅
%A 孙鹏飞
%J Advances in Geosciences
%P 889-902
%@ 2163-3975
%D 2025
%I Hans Publishing
%R 10.12677/ag.2025.156085
%X 欧拉反褶积法作为无需先验信息的位场自动反演方法，因其计算高效、适用性广等特点，被广泛应用于场源体定位与边界识别。然而，噪声干扰引起的虚假解(如高频噪声使反演结果偏移真实场源)及复杂场源耦合效应(如多尺度异常叠加降低反演精度)等问题，严重制约其在深部构造精细解析中的应用。针对上述不足，文章提出“小波多尺度分析 + 欧拉反褶积联合反演”框架，通过理论建模、对比实验与实例应用验证其技术优势，最终在松辽盆地北部划分出8条主要断裂。为复杂地质区域断裂划分提供新的技术路径。
The Euler deconvolution method, as a prior-information-free automatic potential field inversion technique, has been widely applied in source body positioning and boundary identification due to its computational efficiency and broad applicability. However, issues such as spurious solutions caused by noise interference (e.g., high-frequency noise deflecting inversion results from true field sources) and complex source coupling effects (e.g., multi-scale anomaly superposition reducing inversion accuracy) significantly limit its application in detailed deep structural analysis. To address these shortcomings, this paper proposes a “wavelet multiscale analysis + Euler deconvolution joint inversion” framework, verifying its technical advantages through theoretical modeling, comparative experiments, and practical applications, and ultimately delineating 8 major faults in the northern Songliao Basin. This provides a novel technical approach for fault mapping in complex geological regions.
%K 欧拉反褶积，
%K 小波多尺度分析，
%K 噪声抑制，
%K 断裂划分
Euler Deconvolution
%K Wavelet Multi-Scale Analysis
%K Noise Suppression
%K Deep Structural Analysis
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=118213